Since products are often referred to by a number of different names or titles, identifying references to the same in unstructured text is a challenging problem. For example, users searching web-based content may refer to a product by a phrase that is a variant of, or syntactically unconnected with, the title, including a phrase that might refer to a group of products. Further, a product may be referred to by different names from one webpage to another or a product may be referred to by multiple different names within a single webpage. Additionally, it is also often difficult to distinguish between cases where a phrase is referring to a product or is simply being used ordinarily in everyday language. Accordingly, there is a need to be able to more adequately identify product references in unstructured text that are a variant of, or syntactically unconnected with, a product's title for applications ranging from contextually targeted advertisements to monetizing content via affiliate marketing networks to improving the overall quality and efficiency of search engines. Therefore, as technology advances and as people are increasingly relying on computing devices in a wider variety of ways, it can be advantageous to adapt the ways in which these product references are identified and extracted from unstructured text.